Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi

Approximate explicit feature map for computational augmentation of RGB images of hematoxylin and eosin stained histopathological specimens (CROSBI ID 699883)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Kopriva, Ivica ; Sitnik, Dario ; Aralica, Gorana ; Paćić, Arijana ; Popović Hadžija, Marijana ; Hadžija, Mirko ; Approximate explicit feature map for computational augmentation of RGB images of hematoxylin and eosin stained histopathological specimens // Medical Imaging 2021: Digital Pathology, vol. 11603 / Tomaszevski, John ; Ward, Aaron (ur.). Belingham: SPIE, 2021. doi: 10.1117/12.2579408

Podaci o odgovornosti

Kopriva, Ivica ; Sitnik, Dario ; Aralica, Gorana ; Paćić, Arijana ; Popović Hadžija, Marijana ; Hadžija, Mirko ;

engleski

Approximate explicit feature map for computational augmentation of RGB images of hematoxylin and eosin stained histopathological specimens

Hyperspectral imaging (HSI) is demonstrating the growing capability for disease diagnosis and surgical cancer resection. That is mainly due to high spectral resolution of HSI when compared with its color (RGB) counterparts. However, increased spectral resolution is often associated with the loss of spatial resolution. That combined with high cost hinders applicability of HSI. Herein, we propose computational approach that attempts to mimic the HSI. It is using an approximate explicit feature map (aEFM) to augment raw and/or stain normalized RGB images of the hematoxylin and eosin stained histopathological specimen. We demonstrate on two public labeled datasets, related to breast cancer and nuclei, the statistically significant improvement of performance of binary (caner vs. non-cancer) segmentation of augmented RGB images in comparison with the results achieved on their RGB counterparts. For the breast cancer, balanced accuracy is increased from 76.56%+/-9.05% to 80.42%+/-9.23% and F1 score from 13.34%+/-6.46% to 17.33%+/-6.36%. For nuclei, balanced accuracy is increased from 68.68%+/-9.25% to 79.99%+/-8.77% and F1 score from 46.92%+/-15.10% to 63.31%+/-14.50%. While constrained nonnegative matrix factorization was used for binary segmentation herein, we conjecture that aEFM based augmentation of RGB images can improve performance of more sophisticated segmentation methods such as deep networks.

hyperspectral microscopic image ; RGB microscopic image ; explicit feature map ; computational augmentation ; segmentation ; histopathology

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

1160301

2021.

objavljeno

10.1117/12.2579408

Podaci o matičnoj publikaciji

Medical Imaging 2021: Digital Pathology, vol. 11603

Tomaszevski, John ; Ward, Aaron

Belingham: SPIE

978-151-064035-1

1605-7422

2140-9045

Podaci o skupu

SPIE Medical Imaging 2021

predavanje

15.02.2021-20.02.2021

San Diego (CA), Sjedinjene Američke Države

Povezanost rada

Računarstvo, Temeljne medicinske znanosti

Poveznice